Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

Numeric10
Categorical8
Boolean2

Alerts

Person ID is uniformly distributed Uniform
Person ID has unique values Unique
Years of Work Experience has 959 (9.6%) zeros Zeros

Reproduction

Analysis started2025-02-05 21:53:32.882766
Analysis finished2025-02-05 21:53:39.894411
Duration7.01 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Person ID
Real number (ℝ)

Uniform  Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:39.941054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2025-02-05T22:53:40.015210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9984 1
 
< 0.1%
9983 1
 
< 0.1%
9982 1
 
< 0.1%
9981 1
 
< 0.1%
9980 1
 
< 0.1%
9979 1
 
< 0.1%
9978 1
 
< 0.1%
9977 1
 
< 0.1%
9976 1
 
< 0.1%
9975 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

Age
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.4943
Minimum21
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:40.071254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median27
Q331
95-th percentile34
Maximum34
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.0318225
Coefficient of variation (CV)0.14664212
Kurtosis-1.2138345
Mean27.4943
Median Absolute Deviation (MAD)3
Skewness-0.0017594927
Sum274943
Variance16.255593
MonotonicityNot monotonic
2025-02-05T22:53:40.126203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
33 755
 
7.5%
26 745
 
7.4%
32 735
 
7.3%
21 734
 
7.3%
30 733
 
7.3%
27 719
 
7.2%
23 715
 
7.1%
25 709
 
7.1%
28 709
 
7.1%
24 704
 
7.0%
Other values (4) 2742
27.4%
ValueCountFrequency (%)
21 734
7.3%
22 696
7.0%
23 715
7.1%
24 704
7.0%
25 709
7.1%
26 745
7.4%
27 719
7.2%
28 709
7.1%
29 689
6.9%
30 733
7.3%
ValueCountFrequency (%)
34 681
6.8%
33 755
7.5%
32 735
7.3%
31 676
6.8%
30 733
7.3%
29 689
6.9%
28 709
7.1%
27 719
7.2%
26 745
7.4%
25 709
7.1%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Male
5073 
Female
4460 
Other
 
467

Length

Max length6
Median length4
Mean length4.9387
Min length4

Characters and Unicode

Total characters49387
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male 5073
50.7%
Female 4460
44.6%
Other 467
 
4.7%

Length

2025-02-05T22:53:40.187263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:40.228022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 5073
50.7%
female 4460
44.6%
other 467
 
4.7%

Most occurring characters

ValueCountFrequency (%)
e 14460
29.3%
a 9533
19.3%
l 9533
19.3%
M 5073
 
10.3%
F 4460
 
9.0%
m 4460
 
9.0%
O 467
 
0.9%
t 467
 
0.9%
h 467
 
0.9%
r 467
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14460
29.3%
a 9533
19.3%
l 9533
19.3%
M 5073
 
10.3%
F 4460
 
9.0%
m 4460
 
9.0%
O 467
 
0.9%
t 467
 
0.9%
h 467
 
0.9%
r 467
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14460
29.3%
a 9533
19.3%
l 9533
19.3%
M 5073
 
10.3%
F 4460
 
9.0%
m 4460
 
9.0%
O 467
 
0.9%
t 467
 
0.9%
h 467
 
0.9%
r 467
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14460
29.3%
a 9533
19.3%
l 9533
19.3%
M 5073
 
10.3%
F 4460
 
9.0%
m 4460
 
9.0%
O 467
 
0.9%
t 467
 
0.9%
h 467
 
0.9%
r 467
 
0.9%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Economics
2082 
Science
2065 
Arts
1955 
Engineering
1952 
Business
1946 

Length

Max length11
Median length8
Mean length7.8053
Min length4

Characters and Unicode

Total characters78053
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArts
2nd rowArts
3rd rowBusiness
4th rowEngineering
5th rowBusiness

Common Values

ValueCountFrequency (%)
Economics 2082
20.8%
Science 2065
20.6%
Arts 1955
19.6%
Engineering 1952
19.5%
Business 1946
19.5%

Length

2025-02-05T22:53:40.275901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:40.319620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
economics 2082
20.8%
science 2065
20.6%
arts 1955
19.6%
engineering 1952
19.5%
business 1946
19.5%

Most occurring characters

ValueCountFrequency (%)
n 11949
15.3%
i 9997
12.8%
e 9980
12.8%
s 9875
12.7%
c 8294
10.6%
o 4164
 
5.3%
E 4034
 
5.2%
r 3907
 
5.0%
g 3904
 
5.0%
m 2082
 
2.7%
Other values (5) 9867
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 11949
15.3%
i 9997
12.8%
e 9980
12.8%
s 9875
12.7%
c 8294
10.6%
o 4164
 
5.3%
E 4034
 
5.2%
r 3907
 
5.0%
g 3904
 
5.0%
m 2082
 
2.7%
Other values (5) 9867
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 11949
15.3%
i 9997
12.8%
e 9980
12.8%
s 9875
12.7%
c 8294
10.6%
o 4164
 
5.3%
E 4034
 
5.2%
r 3907
 
5.0%
g 3904
 
5.0%
m 2082
 
2.7%
Other values (5) 9867
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 11949
15.3%
i 9997
12.8%
e 9980
12.8%
s 9875
12.7%
c 8294
10.6%
o 4164
 
5.3%
E 4034
 
5.2%
r 3907
 
5.0%
g 3904
 
5.0%
m 2082
 
2.7%
Other values (5) 9867
12.6%

Undergraduate GPA
Real number (ℝ)

Distinct201
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.991611
Minimum2
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:40.385309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.49
median2.99
Q33.48
95-th percentile3.9
Maximum4
Range2
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.57350877
Coefficient of variation (CV)0.19170566
Kurtosis-1.1871338
Mean2.991611
Median Absolute Deviation (MAD)0.49
Skewness0.019692306
Sum29916.11
Variance0.32891231
MonotonicityNot monotonic
2025-02-05T22:53:40.459251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.77 71
 
0.7%
2.43 70
 
0.7%
2.82 69
 
0.7%
3.64 68
 
0.7%
3.29 65
 
0.7%
3.32 63
 
0.6%
2.12 63
 
0.6%
2.19 63
 
0.6%
2.52 62
 
0.6%
2.05 62
 
0.6%
Other values (191) 9344
93.4%
ValueCountFrequency (%)
2 20
 
0.2%
2.01 39
0.4%
2.02 57
0.6%
2.03 34
0.3%
2.04 55
0.5%
2.05 62
0.6%
2.06 55
0.5%
2.07 44
0.4%
2.08 46
0.5%
2.09 49
0.5%
ValueCountFrequency (%)
4 28
0.3%
3.99 50
0.5%
3.98 52
0.5%
3.97 43
0.4%
3.96 49
0.5%
3.95 43
0.4%
3.94 54
0.5%
3.93 46
0.5%
3.92 47
0.5%
3.91 54
0.5%

Years of Work Experience
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5335
Minimum0
Maximum9
Zeros959
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:40.515942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8616598
Coefficient of variation (CV)0.63122527
Kurtosis-1.2150048
Mean4.5335
Median Absolute Deviation (MAD)2
Skewness-0.009684588
Sum45335
Variance8.1890967
MonotonicityNot monotonic
2025-02-05T22:53:40.559201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 1040
10.4%
9 1025
10.2%
3 1012
10.1%
7 1009
10.1%
2 1002
10.0%
4 996
10.0%
5 995
10.0%
1 984
9.8%
8 978
9.8%
0 959
9.6%
ValueCountFrequency (%)
0 959
9.6%
1 984
9.8%
2 1002
10.0%
3 1012
10.1%
4 996
10.0%
5 995
10.0%
6 1040
10.4%
7 1009
10.1%
8 978
9.8%
9 1025
10.2%
ValueCountFrequency (%)
9 1025
10.2%
8 978
9.8%
7 1009
10.1%
6 1040
10.4%
5 995
10.0%
4 996
10.0%
3 1012
10.1%
2 1002
10.0%
1 984
9.8%
0 959
9.6%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Entrepreneur
2034 
Analyst
2006 
Engineer
2003 
Consultant
1989 
Manager
1968 

Length

Max length12
Median length10
Mean length8.814
Min length7

Characters and Unicode

Total characters88140
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntrepreneur
2nd rowAnalyst
3rd rowEngineer
4th rowManager
5th rowEntrepreneur

Common Values

ValueCountFrequency (%)
Entrepreneur 2034
20.3%
Analyst 2006
20.1%
Engineer 2003
20.0%
Consultant 1989
19.9%
Manager 1968
19.7%

Length

2025-02-05T22:53:40.611704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:40.656121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
entrepreneur 2034
20.3%
analyst 2006
20.1%
engineer 2003
20.0%
consultant 1989
19.9%
manager 1968
19.7%

Most occurring characters

ValueCountFrequency (%)
n 16026
18.2%
e 12076
13.7%
r 10073
11.4%
t 8018
9.1%
a 7931
9.0%
E 4037
 
4.6%
u 4023
 
4.6%
s 3995
 
4.5%
l 3995
 
4.5%
g 3971
 
4.5%
Other values (7) 13995
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 16026
18.2%
e 12076
13.7%
r 10073
11.4%
t 8018
9.1%
a 7931
9.0%
E 4037
 
4.6%
u 4023
 
4.6%
s 3995
 
4.5%
l 3995
 
4.5%
g 3971
 
4.5%
Other values (7) 13995
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 16026
18.2%
e 12076
13.7%
r 10073
11.4%
t 8018
9.1%
a 7931
9.0%
E 4037
 
4.6%
u 4023
 
4.6%
s 3995
 
4.5%
l 3995
 
4.5%
g 3971
 
4.5%
Other values (7) 13995
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 16026
18.2%
e 12076
13.7%
r 10073
11.4%
t 8018
9.1%
a 7931
9.0%
E 4037
 
4.6%
u 4023
 
4.6%
s 3995
 
4.5%
l 3995
 
4.5%
g 3971
 
4.5%
Other values (7) 13995
15.9%
Distinct9456
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75166.406
Minimum30013
Maximum119966
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:40.721398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30013
5-th percentile34857.45
Q152862
median74829.5
Q397273
95-th percentile115522.1
Maximum119966
Range89953
Interquartile range (IQR)44411

Descriptive statistics

Standard deviation25850.071
Coefficient of variation (CV)0.34390457
Kurtosis-1.191549
Mean75166.406
Median Absolute Deviation (MAD)22167
Skewness0.0002858567
Sum7.5166406 × 108
Variance6.6822615 × 108
MonotonicityNot monotonic
2025-02-05T22:53:40.793406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102062 5
 
0.1%
87551 3
 
< 0.1%
105193 3
 
< 0.1%
89391 3
 
< 0.1%
107470 3
 
< 0.1%
92424 3
 
< 0.1%
96607 3
 
< 0.1%
108827 3
 
< 0.1%
82095 3
 
< 0.1%
94199 3
 
< 0.1%
Other values (9446) 9968
99.7%
ValueCountFrequency (%)
30013 1
< 0.1%
30017 1
< 0.1%
30037 1
< 0.1%
30046 1
< 0.1%
30051 1
< 0.1%
30067 1
< 0.1%
30069 1
< 0.1%
30083 1
< 0.1%
30099 1
< 0.1%
30123 1
< 0.1%
ValueCountFrequency (%)
119966 1
< 0.1%
119964 1
< 0.1%
119960 1
< 0.1%
119959 1
< 0.1%
119949 1
< 0.1%
119939 1
< 0.1%
119935 1
< 0.1%
119934 1
< 0.1%
119930 1
< 0.1%
119919 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6009 
True
3991 
ValueCountFrequency (%)
False 6009
60.1%
True 3991
39.9%
2025-02-05T22:53:40.838319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

GRE/GMAT Score
Real number (ℝ)

Distinct550
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.4469
Minimum250
Maximum799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:40.885942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile278
Q1390
median524
Q3661
95-th percentile771
Maximum799
Range549
Interquartile range (IQR)271

Descriptive statistics

Standard deviation158.06376
Coefficient of variation (CV)0.30139135
Kurtosis-1.1956303
Mean524.4469
Median Absolute Deviation (MAD)136
Skewness0.0075348688
Sum5244469
Variance24984.152
MonotonicityNot monotonic
2025-02-05T22:53:40.954527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 30
 
0.3%
408 30
 
0.3%
606 28
 
0.3%
706 28
 
0.3%
438 28
 
0.3%
416 27
 
0.3%
699 27
 
0.3%
362 27
 
0.3%
311 27
 
0.3%
272 27
 
0.3%
Other values (540) 9721
97.2%
ValueCountFrequency (%)
250 16
0.2%
251 15
0.1%
252 17
0.2%
253 21
0.2%
254 14
0.1%
255 15
0.1%
256 21
0.2%
257 16
0.2%
258 19
0.2%
259 24
0.2%
ValueCountFrequency (%)
799 19
0.2%
798 15
0.1%
797 16
0.2%
796 19
0.2%
795 16
0.2%
794 21
0.2%
793 17
0.2%
792 11
0.1%
791 20
0.2%
790 25
0.2%
Distinct499
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.0362
Minimum1
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:41.026068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1121
median243
Q3373
95-th percentile476
Maximum499
Range498
Interquartile range (IQR)252

Descriptive statistics

Standard deviation144.87624
Coefficient of variation (CV)0.58645753
Kurtosis-1.1978612
Mean247.0362
Median Absolute Deviation (MAD)126
Skewness0.039464317
Sum2470362
Variance20989.125
MonotonicityNot monotonic
2025-02-05T22:53:41.099390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 35
 
0.4%
133 33
 
0.3%
233 32
 
0.3%
427 32
 
0.3%
186 32
 
0.3%
234 32
 
0.3%
486 31
 
0.3%
267 31
 
0.3%
91 30
 
0.3%
266 30
 
0.3%
Other values (489) 9682
96.8%
ValueCountFrequency (%)
1 26
0.3%
2 19
0.2%
3 16
0.2%
4 21
0.2%
5 26
0.3%
6 13
0.1%
7 26
0.3%
8 17
0.2%
9 21
0.2%
10 27
0.3%
ValueCountFrequency (%)
499 21
0.2%
498 24
0.2%
497 28
0.3%
496 24
0.2%
495 23
0.2%
494 18
0.2%
493 20
0.2%
492 17
0.2%
491 23
0.2%
490 24
0.2%

Entrepreneurial Interest
Real number (ℝ)

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.47783
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:41.171337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q13.2
median5.5
Q37.7
95-th percentile9.505
Maximum10
Range9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.5858643
Coefficient of variation (CV)0.47205998
Kurtosis-1.1923117
Mean5.47783
Median Absolute Deviation (MAD)2.2
Skewness0.0103027
Sum54778.3
Variance6.6866942
MonotonicityNot monotonic
2025-02-05T22:53:41.242000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 138
 
1.4%
1.6 131
 
1.3%
4.1 131
 
1.3%
7.5 127
 
1.3%
3.3 127
 
1.3%
1.2 125
 
1.2%
3.2 124
 
1.2%
6.1 124
 
1.2%
5.7 121
 
1.2%
6 121
 
1.2%
Other values (81) 8731
87.3%
ValueCountFrequency (%)
1 53
0.5%
1.1 89
0.9%
1.2 125
1.2%
1.3 107
1.1%
1.4 112
1.1%
1.5 113
1.1%
1.6 131
1.3%
1.7 105
1.1%
1.8 112
1.1%
1.9 114
1.1%
ValueCountFrequency (%)
10 54
0.5%
9.9 105
1.1%
9.8 120
1.2%
9.7 114
1.1%
9.6 107
1.1%
9.5 85
0.9%
9.4 117
1.2%
9.3 110
1.1%
9.2 93
0.9%
9.1 103
1.0%

Networking Importance
Real number (ℝ)

Distinct91
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.52217
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:41.315246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q13.3
median5.5
Q37.7
95-th percentile9.6
Maximum10
Range9
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.5884222
Coefficient of variation (CV)0.4687328
Kurtosis-1.1851145
Mean5.52217
Median Absolute Deviation (MAD)2.2
Skewness-0.015318098
Sum55221.7
Variance6.6999295
MonotonicityNot monotonic
2025-02-05T22:53:41.386801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 141
 
1.4%
8.7 132
 
1.3%
3.9 131
 
1.3%
5.6 130
 
1.3%
6.4 130
 
1.3%
7.4 129
 
1.3%
9.9 127
 
1.3%
8.1 126
 
1.3%
2.9 124
 
1.2%
6.5 124
 
1.2%
Other values (81) 8706
87.1%
ValueCountFrequency (%)
1 60
0.6%
1.1 118
1.2%
1.2 99
1.0%
1.3 108
1.1%
1.4 97
1.0%
1.5 107
1.1%
1.6 109
1.1%
1.7 120
1.2%
1.8 102
1.0%
1.9 115
1.1%
ValueCountFrequency (%)
10 49
 
0.5%
9.9 127
1.3%
9.8 103
1.0%
9.7 123
1.2%
9.6 103
1.0%
9.5 118
1.2%
9.4 96
1.0%
9.3 118
1.2%
9.2 106
1.1%
9.1 108
1.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Employer
2538 
Loan
2531 
Scholarship
2501 
Self-funded
2430 

Length

Max length11
Median length8
Mean length8.4669
Min length4

Characters and Unicode

Total characters84669
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoan
2nd rowLoan
3rd rowScholarship
4th rowLoan
5th rowLoan

Common Values

ValueCountFrequency (%)
Employer 2538
25.4%
Loan 2531
25.3%
Scholarship 2501
25.0%
Self-funded 2430
24.3%

Length

2025-02-05T22:53:41.450749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:41.491890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
employer 2538
25.4%
loan 2531
25.3%
scholarship 2501
25.0%
self-funded 2430
24.3%

Most occurring characters

ValueCountFrequency (%)
o 7570
 
8.9%
l 7469
 
8.8%
e 7398
 
8.7%
p 5039
 
6.0%
r 5039
 
6.0%
a 5032
 
5.9%
h 5002
 
5.9%
n 4961
 
5.9%
S 4931
 
5.8%
f 4860
 
5.7%
Other values (10) 27368
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84669
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 7570
 
8.9%
l 7469
 
8.8%
e 7398
 
8.7%
p 5039
 
6.0%
r 5039
 
6.0%
a 5032
 
5.9%
h 5002
 
5.9%
n 4961
 
5.9%
S 4931
 
5.8%
f 4860
 
5.7%
Other values (10) 27368
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84669
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 7570
 
8.9%
l 7469
 
8.8%
e 7398
 
8.7%
p 5039
 
6.0%
r 5039
 
6.0%
a 5032
 
5.9%
h 5002
 
5.9%
n 4961
 
5.9%
S 4931
 
5.8%
f 4860
 
5.7%
Other values (10) 27368
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84669
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 7570
 
8.9%
l 7469
 
8.8%
e 7398
 
8.7%
p 5039
 
6.0%
r 5039
 
6.0%
a 5032
 
5.9%
h 5002
 
5.9%
n 4961
 
5.9%
S 4931
 
5.8%
f 4860
 
5.7%
Other values (10) 27368
32.3%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Executive
2041 
Marketing Director
2027 
Consultant
1986 
Startup Founder
1978 
Finance Manager
1968 

Length

Max length18
Median length15
Mean length13.3905
Min length9

Characters and Unicode

Total characters133905
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinance Manager
2nd rowStartup Founder
3rd rowConsultant
4th rowConsultant
5th rowConsultant

Common Values

ValueCountFrequency (%)
Executive 2041
20.4%
Marketing Director 2027
20.3%
Consultant 1986
19.9%
Startup Founder 1978
19.8%
Finance Manager 1968
19.7%

Length

2025-02-05T22:53:41.545476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:41.586497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
executive 2041
12.8%
marketing 2027
12.7%
director 2027
12.7%
consultant 1986
12.4%
startup 1978
12.4%
founder 1978
12.4%
finance 1968
12.3%
manager 1968
12.3%

Most occurring characters

ValueCountFrequency (%)
e 14050
10.5%
t 14023
10.5%
n 13881
10.4%
r 12005
 
9.0%
a 11895
 
8.9%
i 8063
 
6.0%
u 7983
 
6.0%
c 6036
 
4.5%
o 5991
 
4.5%
5973
 
4.5%
Other values (14) 34005
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 133905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14050
10.5%
t 14023
10.5%
n 13881
10.4%
r 12005
 
9.0%
a 11895
 
8.9%
i 8063
 
6.0%
u 7983
 
6.0%
c 6036
 
4.5%
o 5991
 
4.5%
5973
 
4.5%
Other values (14) 34005
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 133905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14050
10.5%
t 14023
10.5%
n 13881
10.4%
r 12005
 
9.0%
a 11895
 
8.9%
i 8063
 
6.0%
u 7983
 
6.0%
c 6036
 
4.5%
o 5991
 
4.5%
5973
 
4.5%
Other values (14) 34005
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 133905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14050
10.5%
t 14023
10.5%
n 13881
10.4%
r 12005
 
9.0%
a 11895
 
8.9%
i 8063
 
6.0%
u 7983
 
6.0%
c 6036
 
4.5%
o 5991
 
4.5%
5973
 
4.5%
Other values (14) 34005
25.4%

Expected Post-MBA Salary
Real number (ℝ)

Distinct9614
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130321.23
Minimum60001
Maximum199999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-02-05T22:53:41.881979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60001
5-th percentile67196.25
Q195320.5
median130155.5
Q3165757
95-th percentile193293
Maximum199999
Range139998
Interquartile range (IQR)70436.5

Descriptive statistics

Standard deviation40598.422
Coefficient of variation (CV)0.31152578
Kurtosis-1.2095343
Mean130321.23
Median Absolute Deviation (MAD)35223
Skewness-0.00023135815
Sum1.3032123 × 109
Variance1.6482319 × 109
MonotonicityNot monotonic
2025-02-05T22:53:41.955347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131677 3
 
< 0.1%
160308 3
 
< 0.1%
75646 3
 
< 0.1%
146400 3
 
< 0.1%
89976 3
 
< 0.1%
197611 3
 
< 0.1%
127597 3
 
< 0.1%
89534 2
 
< 0.1%
164278 2
 
< 0.1%
120534 2
 
< 0.1%
Other values (9604) 9973
99.7%
ValueCountFrequency (%)
60001 1
< 0.1%
60005 1
< 0.1%
60009 1
< 0.1%
60021 1
< 0.1%
60028 1
< 0.1%
60043 1
< 0.1%
60061 1
< 0.1%
60062 1
< 0.1%
60063 1
< 0.1%
60072 1
< 0.1%
ValueCountFrequency (%)
199999 1
< 0.1%
199998 1
< 0.1%
199963 1
< 0.1%
199961 1
< 0.1%
199947 1
< 0.1%
199940 1
< 0.1%
199899 1
< 0.1%
199868 1
< 0.1%
199858 1
< 0.1%
199855 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
International
5083 
Domestic
4917 

Length

Max length13
Median length13
Mean length10.5415
Min length8

Characters and Unicode

Total characters105415
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInternational
2nd rowInternational
3rd rowDomestic
4th rowInternational
5th rowDomestic

Common Values

ValueCountFrequency (%)
International 5083
50.8%
Domestic 4917
49.2%

Length

2025-02-05T22:53:42.022189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:42.057766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
international 5083
50.8%
domestic 4917
49.2%

Most occurring characters

ValueCountFrequency (%)
n 15249
14.5%
t 15083
14.3%
a 10166
9.6%
i 10000
9.5%
e 10000
9.5%
o 10000
9.5%
I 5083
 
4.8%
r 5083
 
4.8%
l 5083
 
4.8%
D 4917
 
4.7%
Other values (3) 14751
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 15249
14.5%
t 15083
14.3%
a 10166
9.6%
i 10000
9.5%
e 10000
9.5%
o 10000
9.5%
I 5083
 
4.8%
r 5083
 
4.8%
l 5083
 
4.8%
D 4917
 
4.7%
Other values (3) 14751
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 15249
14.5%
t 15083
14.3%
a 10166
9.6%
i 10000
9.5%
e 10000
9.5%
o 10000
9.5%
I 5083
 
4.8%
r 5083
 
4.8%
l 5083
 
4.8%
D 4917
 
4.7%
Other values (3) 14751
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 15249
14.5%
t 15083
14.3%
a 10166
9.6%
i 10000
9.5%
e 10000
9.5%
o 10000
9.5%
I 5083
 
4.8%
r 5083
 
4.8%
l 5083
 
4.8%
D 4917
 
4.7%
Other values (3) 14751
14.0%

Reason for MBA
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Networking
2546 
Career Growth
2519 
Skill Enhancement
2513 
Entrepreneurship
2422 

Length

Max length17
Median length16
Mean length13.968
Min length10

Characters and Unicode

Total characters139680
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntrepreneurship
2nd rowCareer Growth
3rd rowSkill Enhancement
4th rowEntrepreneurship
5th rowSkill Enhancement

Common Values

ValueCountFrequency (%)
Networking 2546
25.5%
Career Growth 2519
25.2%
Skill Enhancement 2513
25.1%
Entrepreneurship 2422
24.2%

Length

2025-02-05T22:53:42.106365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:42.148813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
networking 2546
16.9%
career 2519
16.8%
growth 2519
16.8%
skill 2513
16.7%
enhancement 2513
16.7%
entrepreneurship 2422
16.1%

Most occurring characters

ValueCountFrequency (%)
e 19876
14.2%
r 17369
12.4%
n 14929
 
10.7%
t 10000
 
7.2%
i 7481
 
5.4%
h 7454
 
5.3%
o 5065
 
3.6%
w 5065
 
3.6%
k 5059
 
3.6%
5032
 
3.6%
Other values (13) 42350
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 19876
14.2%
r 17369
12.4%
n 14929
 
10.7%
t 10000
 
7.2%
i 7481
 
5.4%
h 7454
 
5.3%
o 5065
 
3.6%
w 5065
 
3.6%
k 5059
 
3.6%
5032
 
3.6%
Other values (13) 42350
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 19876
14.2%
r 17369
12.4%
n 14929
 
10.7%
t 10000
 
7.2%
i 7481
 
5.4%
h 7454
 
5.3%
o 5065
 
3.6%
w 5065
 
3.6%
k 5059
 
3.6%
5032
 
3.6%
Other values (13) 42350
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 19876
14.2%
r 17369
12.4%
n 14929
 
10.7%
t 10000
 
7.2%
i 7481
 
5.4%
h 7454
 
5.3%
o 5065
 
3.6%
w 5065
 
3.6%
k 5059
 
3.6%
5032
 
3.6%
Other values (13) 42350
30.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
On-Campus
5005 
Online
4995 

Length

Max length9
Median length9
Mean length7.5015
Min length6

Characters and Unicode

Total characters75015
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOn-Campus
2nd rowOnline
3rd rowOnline
4th rowOn-Campus
5th rowOnline

Common Values

ValueCountFrequency (%)
On-Campus 5005
50.0%
Online 4995
50.0%

Length

2025-02-05T22:53:42.205568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-05T22:53:42.240990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
on-campus 5005
50.0%
online 4995
50.0%

Most occurring characters

ValueCountFrequency (%)
n 14995
20.0%
O 10000
13.3%
- 5005
 
6.7%
C 5005
 
6.7%
a 5005
 
6.7%
m 5005
 
6.7%
p 5005
 
6.7%
u 5005
 
6.7%
s 5005
 
6.7%
l 4995
 
6.7%
Other values (2) 9990
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 14995
20.0%
O 10000
13.3%
- 5005
 
6.7%
C 5005
 
6.7%
a 5005
 
6.7%
m 5005
 
6.7%
p 5005
 
6.7%
u 5005
 
6.7%
s 5005
 
6.7%
l 4995
 
6.7%
Other values (2) 9990
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 14995
20.0%
O 10000
13.3%
- 5005
 
6.7%
C 5005
 
6.7%
a 5005
 
6.7%
m 5005
 
6.7%
p 5005
 
6.7%
u 5005
 
6.7%
s 5005
 
6.7%
l 4995
 
6.7%
Other values (2) 9990
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 14995
20.0%
O 10000
13.3%
- 5005
 
6.7%
C 5005
 
6.7%
a 5005
 
6.7%
m 5005
 
6.7%
p 5005
 
6.7%
u 5005
 
6.7%
s 5005
 
6.7%
l 4995
 
6.7%
Other values (2) 9990
13.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
True
5907 
False
4093 
ValueCountFrequency (%)
True 5907
59.1%
False 4093
40.9%
2025-02-05T22:53:42.266426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

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Correlations

2025-02-05T22:53:42.310437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAnnual Salary (Before MBA)Current Job TitleDecided to Pursue MBA?Desired Post-MBA RoleEntrepreneurial InterestExpected Post-MBA SalaryGRE/GMAT ScoreGenderHas Management ExperienceLocation Preference (Post-MBA)MBA Funding SourceNetworking ImportanceOnline vs. On-Campus MBAPerson IDReason for MBAUndergrad University RankingUndergraduate GPAUndergraduate MajorYears of Work Experience
Age1.0000.0110.0000.0000.0000.0100.0160.0020.0000.0000.0000.000-0.0070.0000.0060.000-0.0040.0150.000-0.001
Annual Salary (Before MBA)0.0111.0000.0000.0000.000-0.0000.020-0.0060.0000.0000.0210.0100.0020.0000.0020.000-0.0010.0020.015-0.022
Current Job Title0.0000.0001.0000.0000.0000.0000.0180.0000.0110.0000.0000.0000.0000.0190.0000.0030.0120.0090.0000.000
Decided to Pursue MBA?0.0000.0000.0001.0000.0000.0000.0240.0000.0000.0000.0150.0000.0000.0000.0000.0130.0000.0000.0070.020
Desired Post-MBA Role0.0000.0000.0000.0001.0000.0000.0100.0060.0000.0000.0000.0000.0000.0000.0180.0170.0000.0180.0000.000
Entrepreneurial Interest0.010-0.0000.0000.0000.0001.0000.003-0.0200.0090.0030.0000.010-0.0020.008-0.0050.0110.0090.0140.0000.012
Expected Post-MBA Salary0.0160.0200.0180.0240.0100.0031.0000.0040.0210.0250.0000.0000.0030.0000.0020.0000.011-0.0010.017-0.002
GRE/GMAT Score0.002-0.0060.0000.0000.006-0.0200.0041.0000.0000.0000.0220.000-0.0070.0000.0210.000-0.0030.0130.0000.015
Gender0.0000.0000.0110.0000.0000.0090.0210.0001.0000.0000.0000.0210.0000.0140.0300.0000.0160.0000.0000.000
Has Management Experience0.0000.0000.0000.0000.0000.0030.0250.0000.0001.0000.0000.0130.0000.0000.0000.0000.0000.0250.0200.008
Location Preference (Post-MBA)0.0000.0210.0000.0150.0000.0000.0000.0220.0000.0001.0000.0050.0150.0000.0000.0180.0000.0030.0120.000
MBA Funding Source0.0000.0100.0000.0000.0000.0100.0000.0000.0210.0130.0051.0000.0000.0160.0180.0000.0140.0000.0000.022
Networking Importance-0.0070.0020.0000.0000.000-0.0020.003-0.0070.0000.0000.0150.0001.0000.0270.0040.000-0.0000.0120.000-0.002
Online vs. On-Campus MBA0.0000.0000.0190.0000.0000.0080.0000.0000.0140.0000.0000.0160.0271.0000.0000.0000.0000.0000.0150.000
Person ID0.0060.0020.0000.0000.018-0.0050.0020.0210.0300.0000.0000.0180.0040.0001.0000.0000.0060.0050.0220.002
Reason for MBA0.0000.0000.0030.0130.0170.0110.0000.0000.0000.0000.0180.0000.0000.0000.0001.0000.0000.0170.0120.000
Undergrad University Ranking-0.004-0.0010.0120.0000.0000.0090.011-0.0030.0160.0000.0000.014-0.0000.0000.0060.0001.0000.0020.0150.006
Undergraduate GPA0.0150.0020.0090.0000.0180.014-0.0010.0130.0000.0250.0030.0000.0120.0000.0050.0170.0021.0000.026-0.010
Undergraduate Major0.0000.0150.0000.0070.0000.0000.0170.0000.0000.0200.0120.0000.0000.0150.0220.0120.0150.0261.0000.017
Years of Work Experience-0.001-0.0220.0000.0200.0000.012-0.0020.0150.0000.0080.0000.022-0.0020.0000.0020.0000.006-0.0100.0171.000

Missing values

2025-02-05T22:53:39.712786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-05T22:53:39.821704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Person IDAgeGenderUndergraduate MajorUndergraduate GPAYears of Work ExperienceCurrent Job TitleAnnual Salary (Before MBA)Has Management ExperienceGRE/GMAT ScoreUndergrad University RankingEntrepreneurial InterestNetworking ImportanceMBA Funding SourceDesired Post-MBA RoleExpected Post-MBA SalaryLocation Preference (Post-MBA)Reason for MBAOnline vs. On-Campus MBADecided to Pursue MBA?
0127MaleArts3.188Entrepreneur90624No6881857.97.6LoanFinance Manager156165InternationalEntrepreneurshipOn-CampusYes
1224MaleArts3.034Analyst53576Yes7914053.84.1LoanStartup Founder165612InternationalCareer GrowthOnlineNo
2333FemaleBusiness3.669Engineer79796No4301076.75.5ScholarshipConsultant122248DomesticSkill EnhancementOnlineNo
3431MaleEngineering2.461Manager105956No3562571.05.3LoanConsultant123797InternationalEntrepreneurshipOn-CampusNo
4528FemaleBusiness2.759Entrepreneur96132No4723389.54.9LoanConsultant197509DomesticSkill EnhancementOnlineYes
5633MaleBusiness3.583Manager101925No4092803.47.1ScholarshipMarketing Director99591InternationalNetworkingOn-CampusNo
6725FemaleArts3.065Consultant81962No3691589.28.3Self-fundedExecutive119223InternationalNetworkingOnlineNo
7827MaleEngineering2.806Engineer100072Yes5881902.99.6LoanFinance Manager199447InternationalEntrepreneurshipOn-CampusYes
8930FemaleArts2.066Entrepreneur118689Yes5214559.14.7ScholarshipExecutive76037DomesticCareer GrowthOn-CampusYes
91023FemaleArts3.513Entrepreneur112387No6714116.07.3ScholarshipStartup Founder92294InternationalEntrepreneurshipOnlineYes
Person IDAgeGenderUndergraduate MajorUndergraduate GPAYears of Work ExperienceCurrent Job TitleAnnual Salary (Before MBA)Has Management ExperienceGRE/GMAT ScoreUndergrad University RankingEntrepreneurial InterestNetworking ImportanceMBA Funding SourceDesired Post-MBA RoleExpected Post-MBA SalaryLocation Preference (Post-MBA)Reason for MBAOnline vs. On-Campus MBADecided to Pursue MBA?
9990999123MaleEconomics3.414Manager63239No711668.96.7Self-fundedExecutive193284DomesticNetworkingOn-CampusYes
9991999224FemaleScience3.367Entrepreneur60072No5603541.93.1Self-fundedFinance Manager174191DomesticEntrepreneurshipOnlineNo
9992999331FemaleEngineering3.108Engineer39909Yes3071028.59.9Self-fundedFinance Manager149757DomesticCareer GrowthOn-CampusYes
9993999425MaleArts3.408Entrepreneur76025No3112331.27.7EmployerStartup Founder86385DomesticSkill EnhancementOnlineYes
9994999523MaleBusiness3.287Entrepreneur92456Yes2561901.62.7Self-fundedFinance Manager135949InternationalNetworkingOnlineYes
9995999633FemaleEconomics3.555Analyst109172Yes5241009.28.0LoanStartup Founder69000InternationalNetworkingOnlineYes
9996999730FemaleBusiness2.485Manager82515Yes3303627.48.5ScholarshipConsultant131054DomesticEntrepreneurshipOn-CampusNo
9997999831FemaleEconomics2.868Manager34152Yes6813086.88.8LoanConsultant100806DomesticNetworkingOn-CampusYes
9998999922FemaleArts2.301Engineer61897No4811905.77.7Self-fundedMarketing Director115872InternationalNetworkingOnlineYes
99991000022MaleArts2.862Manager111499No7102268.01.7EmployerMarketing Director103245DomesticCareer GrowthOn-CampusNo